Gradation conversion device, gradation conversion method, and program

ABSTRACT

A gradation conversion device that converts a gradation of an image, includes: dither means for dithering the image by adding random noise to pixel values forming the image; and one-dimensional ΔΣ modulation means for performing one-dimensional ΔΣ modulation on the dithered image.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority from Japanese Patent ApplicationNo. JP 2008-247291 filed in the Japanese Patent Office on Sep. 26, 2008,the entire content of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a gradation conversion device, agradation conversion method, and a program, and specifically to agradation conversion device, a gradation conversion method, and aprogram for downsizing and cost reduction of the device, for example.

2. Background Art

For example, in order to display an image of N-bit pixel values(hereinafter, also referred to as “N-bit image”) by a display device fordisplaying an image of M (smaller than N)-bit pixel values, it isnecessary to convert the N-bit image into an M-bit image, that is,perform gradation conversion of converting the gradation of the image.

As a method of gradation conversion (gradation conversion method) of anN-bit image into an M-bit image, for example, there is a method ofdropping the last (N minus M) bits of the N-bit pixel values and usingthe rest as M-bit pixel values.

Referring to FIGS. 1A to 2B, the gradation conversion method of droppingthe last (N minus M) bits of the N-bit pixel values and using the restas M-bit pixel values will be explained.

FIGS. 1A and 1B show an 8-bit gradation image and pixel values on acertain horizontal line in the image.

That is, FIG. 1A shows the 8-bit gradation image.

In the image in FIG. 1A, with respect to the horizontal direction, fromleft to right, pixel values gradually change from 100 to 200, and thesame pixel values are arranged in the vertical direction.

FIG. 1B is the pixel values on a certain horizontal line in the image inFIG. 1A.

The pixel value at the left end is 100 and pixel values become largertoward the right. Further, the pixel value at the right end is 200.

FIGS. 2A and 2B show a 4-bit image obtained by dropping the last 4 bitsof the 8-bit image in FIG. 1A, and pixel values on a certain horizontalline in the image.

That is, FIG. 2A shows the image quantized into four bits by droppingthe last 4 bits of the 8-bit image in FIG. 1A, and FIG. 2B shows thepixel values on a certain horizontal line in the image.

8 bits can represent 256 (=2⁸) levels, but 4 bits can represent only 16(=2⁴) levels. Accordingly, in gradation conversion of dropping the last4 bits of the 8-bit image, banding that changes of levels are seen likea band is produced.

In a gradation conversion method of preventing production of banding andperforming pseudo representation of the gray scale of the image beforegradation conversion in the image after gradation conversion, that is,for example, as described above, in a 16-level image obtained bygradation conversion of a 256-level image, as methods of representing256 levels by 16 levels visually when a human sees the image, there arethe random dither method, ordered dither method, and error diffusionmethod.

FIGS. 3A and 3B are diagrams for explanation of the random dithermethod.

That is, FIG. 3A shows a configuration example of a gradation conversiondevice in related art of performing gradation conversion according tothe random dither method, and FIG. 3B shows a gradation image obtainedby gradation conversion by the gradation conversion device in FIG. 3A.

In FIG. 3A, the gradation conversion device includes a calculation part11, a random noise output part 12, and a quantization part 13.

To the calculation part 11, for example, pixel values IN(x,y) of therespective pixels (x,y) of an 8-bit image as a target image of gradationconversion (an image before gradation conversion) are supplied in thesequence of raster scan. Note that, the pixel (x,y) indicates a pixel inthe position of xth from the left and yth from the top.

Further, to the calculation part 11, random noise from the random noiseoutput part 12 that generates and outputs random noise is supplied.

The calculation part 11 adds the pixel values IN(x,y) and the randomnoise from the random noise output part and supplies the resultingadditional values to the quantization part 13.

The quantization part 13 quantizes the additional values from thecalculation part 11 into 4 bits, for example, and outputs the resulting4-bit quantized values as pixel values OUT(x,y) of the pixels (x,y) ofthe image after gradation conversion.

In the random dither method, the configuration of the gradationconversion device is simpler, however, as shown in FIG. 3B, noise ishighly visible in the image after gradation conversion because therandom noise is added to the pixel values IN(x,y), and it is difficultto obtain a good quality image.

FIGS. 4A and 4B are diagrams for explanation of the ordered dithermethod.

That is, FIG. 4A shows a configuration example of a gradation conversiondevice in related art of performing gradation conversion according tothe ordered dither method, and FIG. 4B shows a gradation image obtainedby gradation conversion by the gradation conversion device in FIG. 4A.

In FIG. 4A, the gradation conversion device includes a calculation part21, and a quantization part 22.

To the calculation part 21, for example, pixel values IN(x,y) of therespective pixels (x,y) of an 8-bit image as a target image of gradationconversion are supplied in the sequence of raster scan.

Further, to the calculation part 21, a dither matrix is supplied.

The calculation part 21 adds the pixel values IN(x,y) and values of thedither matrix corresponding to the positions (x,y) of the pixels (x,y)having the pixel values IN(x,y), and supplies the resulting additionalvalues to the quantization part 22.

The quantization part 22 quantizes the additional values from thecalculation part 21 into 4 bits, for example, and outputs the resulting4-bit quantized values as pixel values OUT(x,y) of the pixels (x,y) ofthe image after gradation conversion.

In the ordered dither method, the image quality of the image aftergradation conversion can be improved compared to that in the randomdither method, however, as shown in FIG. 4B, a pattern of the dithermatrix may appear in the image after gradation conversion.

FIGS. 5A and 5B are diagrams for explanation of the error diffusionmethod.

That is, FIG. 5A shows a configuration example of a gradation conversiondevice in related art of performing gradation conversion according tothe error diffusion method, and FIG. 5B shows a gradation image obtainedby gradation conversion by the gradation conversion device in FIG. 5A.

In FIG. 5A, the gradation conversion device includes a calculation part31, a quantization part 32, a calculation part 33, and a two-dimensionalfilter 34.

To the calculation part 31, for example, pixel values IN(x,y) of therespective pixels (x,y) of an 8-bit image as a target image of gradationconversion are supplied in the sequence of raster scan.

Further, to the calculation part 31, output of the two-dimensionalfilter 34 is supplied.

The calculation part 31 adds the pixel values IN(x,y) and the output ofthe two-dimensional filter 34, and supplies the resulting additionalvalues to the quantization part 32 and the calculation part 33.

The quantization part 32 quantizes the additional values from thecalculation part 31 into 4 bits, for example, and outputs the resulting4-bit quantized values as pixel values OUT(x,y) of the pixels (x,y) ofthe image after gradation conversion.

Further, the pixel values OUT(x,y) output by the quantization part 32are also supplied to the calculation part 33.

The calculation part 33 obtains quantization errors −Q(x,y) produced bythe quantization in the quantization part by subtracting the pixelvalues OUT(x,y) from the quantization part 32 from the additional valuesfrom the calculation part 31, that is, subtracting the output from thequantization part 32 from the input to the quantization part 32, andsupplies them to the two-dimensional filter 34.

The two-dimensional filter 34 is a two-dimensional filter of filteringsignals, and filters the quantization errors −Q(x,y) from thecalculation part 33 and outputs the filtering results to the calculationpart 31.

In the calculation part 31, the filtering results of the quantizationerrors −Q(x,y) output by the two-dimensional filter 34 and the pixelvalues IN(x,y) are added in the above described manner.

In the gradation conversion device in FIG. 5A, the quantization errors−Q(x,y) are fed back to the input side (calculation part 31) via thetwo-dimensional filter 34, and a two-dimensional ΔΣ modulator is formed.

According to the two-dimensional ΔΣ modulator, the quantization errors−Q(x,y) are diffused (noise-shaped) in an area at higher spatialfrequencies with respect to both of the horizontal direction(x-direction) and the vertical direction (y-direction). As a result, asshown in FIG. 5B, a good quality image compared to those in the randomdither method and the ordered dither method can be obtained as an imageafter gradation conversion.

Note that, regarding a method of performing gradation conversion into agood quality image by the two-dimensional ΔΣ modulator, details thereofare disclosed in Japanese Patent No. 3959698, for example.

SUMMARY OF THE INVENTION

As described above, according to the two-dimensional ΔΣ modulator,gradation conversion into a good quality image can be performed.

However, the two-dimensional ΔΣ modulator has the two-dimensional filter34 as shown in FIG. 5A, and it is necessary for the two-dimensionalfilter 34 to use a line memory that stores the quantization errorsoutput by the calculation part 33 in the past for filtering.

That is, interest is attracted to a certain pixel (x,y) as a pixel ofinterest (x,y), in the two-dimensional filter 34, filtering of thequantization error −Q(x,y) of the pixel of interest (x,y) is performedusing the quantization errors that have been already obtained withrespect to the plural pixels located near the pixel of interest (x,y) onthe same horizontal line (the yth line) as that of the pixel of interest(x,y) and the plural pixels located near the pixel of interest (x,y) onhorizontal lines (e.g., the (y−1)th line, the (y−2)th line, and so on)above the pixel of interest (x,y).

Therefore, in the two-dimensional filter 34, it is necessary to hold thequantization errors of the pixels on the horizontal lines other than theyth line in addition to the quantization errors of the pixels on thesame yth line as the pixel of interest (x,y), and for the purpose, aline memory for plural horizontal lines is necessary.

As described above, in the two-dimensional filter 34, the line memoriesfor plural horizontal lines are necessary, and the gradation conversiondevice in FIG. 5A formed as the two-dimensional ΔΣ modulator isincreased in size and cost.

Thus, it is desirable that gradation conversion providing a high-qualityimage can be performed without using a line memory, and thereby, forexample, downsizing and cost reduction of the device can be realized.

An embodiment of the invention is directed to a gradation conversiondevice or program which converts a gradation of an image, and includesdither means for dithering the image by adding random noise to pixelvalues forming the image, and one-dimensional ΔΣ modulation means forperforming one-dimensional ΔΣ modulation on the dithered image, or aprogram allowing a computer to function as the gradation conversiondevice.

Another embodiment of the invention is directed to a gradationconversion method of a gradation conversion device that converts agradation of an image, including the steps of allowing the gradationconversion device to dither the image by adding random noise to pixelvalues forming the image, and allowing the gradation conversion deviceto perform one-dimensional ΔΣ modulation on the dithered image.

In the above described embodiments of the invention, the image isdithered by adding random noise to pixel values forming the image, andone-dimensional ΔΣ modulation is performed on the dithered image.

The gradation conversion device may be an independent device or aninternal block forming one apparatus.

Further, the program may be provided by transmission via a transmissionmedium or being recorded in a recording medium.

According to the embodiments of the invention, gradation conversion canbe performed. Especially, gradation conversion providing a high qualityimage can be performed without using a line memory.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show an 8-bit gradation image and pixel values on acertain horizontal line in the image.

FIGS. 2A and 2B show a 4-bit image obtained by dropping the last 4 bitsof the 8-bit image, and pixel values on a certain horizontal line in theimage.

FIGS. 3A and 3B are diagrams for explanation of the random dithermethod.

FIGS. 4A and 4B are diagrams for explanation of the ordered dithermethod.

FIGS. 5A and 5B are diagrams for explanation of the error diffusionmethod.

FIG. 6 is a block diagram showing a configuration example of oneembodiment of a TV to which the invention is applied.

FIG. 7 is a block diagram showing a configuration example of a gradationconversion unit 45.

FIG. 8 shows a sequence of pixels (pixel values) as a target ofgradation conversion processing.

FIG. 9 is a flowchart for explanation of the gradation conversionprocessing.

FIGS. 10A and 10B show an image obtained by gradation conversion of thegradation conversion unit 45 and pixel values on a certain horizontalline in the image.

FIG. 11 is a block diagram showing a configuration example of a ditheraddition part 51.

FIG. 12 shows a spatial frequency characteristic of the visual sense ofhuman.

FIG. 13 is a diagram for explanation of the unit cycle/degree of thespatial frequency.

FIGS. 14A and 14B are diagrams for explanation of a method ofdetermining a filter coefficient of an HPF 62 by a coefficient settingpart 64.

FIG. 15 is a block diagram showing a configuration example of aone-dimensional ΔΣ modulation part 52.

FIG. 16 is a block diagram showing a configuration example of aone-dimensional filter.

FIGS. 17A and 17B are diagrams for explanation of a method ofdetermining a filter coefficient of the one-dimensional filter 71performed in a coefficient setting part 72.

FIG. 18 is a block diagram showing another configuration example of theone-dimensional filter 71.

FIG. 19 shows an amplitude characteristic of noise shaping using a Floydfilter and an amplitude characteristic of noise shaping using a Jarvisfilter.

FIG. 20 shows an amplitude characteristic of noise shaping using an SBMfilter.

FIGS. 21A and 21B show a first example of an amplitude characteristic ofnoise shaping and filter coefficients of the one-dimensional filter 71.

FIGS. 22A and 22B show a second example of an amplitude characteristicof noise shaping and filter coefficients of the one-dimensional filter71.

FIGS. 23A and 23B show a third example of an amplitude characteristic ofnoise shaping and filter coefficients of the one-dimensional filter 71.

FIGS. 24A and 24B show a first example of an amplitude characteristic ofthe HPF 62 and filter coefficients of the HPF 62.

FIGS. 25A and 25B show a second example of an amplitude characteristicof the HPF 62 and filter coefficients of the HPF 62.

FIGS. 26A and 26B show a third example of an amplitude characteristic ofthe HPF 62 and filter coefficients of the HPF 62.

FIG. 27 is a block diagram showing a configuration example of oneembodiment of a computer to which the invention is applied.

DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 6 is a block diagram showing a configuration example of oneembodiment of a TV (television receiver) to which the invention isapplied.

In FIG. 6, the TV includes a tuner 41, a demultiplexer 42, a decoder 43,a noise reduction unit 44, a gradation conversion unit 45, a displaycontrol unit 46, and a display unit 47.

The tuner 41 receives broadcast signals of digital broadcasting, forexample, and demodulates the broadcast signals into a transport streamand supplies it to the demultiplexer 42.

The demultiplexer 42 separates a necessary TS (Transport Stream) packetfrom the transport stream from the tuner 41 and supplies it to thedecoder 43.

The decoder 43 decodes MPEG (Moving Picture Expert Group)-encoded datacontained in the TS packet from the demultiplexer 42, and thereby,obtains an 8-bit image (data), for example, and supplies it to the noisereduction unit 44.

The noise reduction unit 44 performs noise reduction processing on an8-bit image from the decoder 43 and supplies a resulting 12-bit image,for example, to the gradation conversion unit 45.

That is, according to the noise reduction processing by the noisereduction unit 44, the 8-bit image is extended to the 12-bit image.

The gradation conversion unit 45 performs gradation conversion ofconverting the 12-bit image supplied from the noise reduction unit 44into an image in a bit number that can be displayed by the display unit47.

That is, the gradation conversion unit 45 acquires necessary informationon the bit number of the image that can be displayed by the display unit47 etc. from the display control unit 46.

If the bit number of the image that can be displayed by the display unit47 is 8 bits, for example, the gradation conversion unit 45 performsgradation conversion of converting the 12-bit image supplied from thenoise reduction unit 44 into an 8-bit image and supplies it to thedisplay control unit 46.

The display control unit 46 controls the display unit 47 and allows thedisplay unit 47 to display the image from the gradation conversion unit45.

The display unit 47 includes an LCD (Liquid Crystal Display), organic EL(organic Electro Luminescence), or the like, for example, and displaysthe image under the control of the display control unit 46.

FIG. 7 shows a configuration example of the gradation conversion unit 45in FIG. 6.

In FIG. 7, the gradation conversion unit 45 includes a dither additionpart 51 and a one-dimensional ΔΣ modulation part 52, and performs thegradation conversion on the image from the noise reduction unit 44 (FIG.6) and supplies it to the display control unit 46 (FIG. 6).

That is, to the dither addition part 51, the image from the noisereduction unit 44 (FIG. 6) is supplied as a target image of gradationconversion (hereinafter, also referred to as “target image”).

The dither addition part 51 performs dithering on the target image byadding random noise to pixel values IN(x,y) forming the target imagefrom the noise reduction unit 44, and supplies it to the one-dimensionalΔΣ modulation part 52.

The one-dimensional ΔΣ modulation part 52 performs one-dimensional ΔΣmodulation on the dithered target image from the dither addition part51, and supplies a resulting image having pixel values OUT(x,y) as animage after gradation conversion to the display control unit 46 (FIG.6).

FIG. 8 shows the sequence of the pixels (pixel values) as a target ofgradation conversion processing in the gradation conversion unit 45 inFIG. 7.

From the noise reduction unit 44 (FIG. 6) to the gradation conversionunit 45, for example, as shown in FIG. 8, the pixel values IN(x,y) ofthe pixels (x,y) of the target image are supplied in the sequence ofraster scan, and therefore, in the gradation conversion unit 45, thepixel values IN(x,y) of the pixels (x,y) of the target image aresubjected to gradation conversion in the sequence of raster scan.

Next, referring to FIG. 9, the gradation conversion processing performedin the gradation conversion unit 45 in FIG. 8 will be explained.

In the gradation conversion processing, the dither addition part 51waits for the supply of the pixel values IN(x,y) of the pixels (x,y) ofthe target image from the noise reduction unit 44 (FIG. 6), performsdithering of adding random noise on the pixel values IN(x,y) at stepS11, and supplies them to the one-dimensional ΔΣ modulation part 52. Theprocess moves to step S12.

At step S12, the one-dimensional ΔΣ modulation part performsone-dimensional ΔΣ modulation on the dithered pixel values from thedither addition part 51 and supplies resulting pixel values OUT(x,y) aspixel values of the image after gradation conversion to the displaycontrol unit 46 (FIG. 6). The process moves to step S13.

At step S13, the gradation conversion unit 45 determines whether thereare pixel values IN(x,y) supplied from the noise reduction unit 44 ornot, if the unit determines there are, the process returns to step S11and the same processing is repeated.

Further, at step S13, if the unit determines there are not pixel valuesIN(x,y) supplied from the noise reduction unit 44, the gradationconversion processing ends.

FIGS. 10A and 10B show an image obtained by gradation conversion of thegradation conversion unit 45 and pixel values on a certain horizontalline in the image.

That is, FIG. 10A shows a 4-bit image (image after gradation conversion)resulted from the gradation conversion of the gradation conversion unit45 on the 8-bit image in FIG. 1A as a target image, and FIG. 10B showsthe pixel values on the certain horizontal line in the 4-bit image aftergradation conversion.

8 bits can represent 256 levels while 4 bits can represent only 16levels. However, in the 4-bit image after gradation conversion by thegradation conversion unit 45, coarse and dense areas having coarse anddense distributions of pixels having pixel values of a certainquantization value Q and pixels having pixel values of a quantizationvalue (Q+1) one larger than the quantization value Q (or a quantizationvalue (Q−1) one smaller than the quantization value Q), i.e., areas witha larger ratio of pixels having pixel values of the quantization value Qand areas with a larger ratio of pixels having pixel values of thequantization value (Q+1) (areas with a smaller ratio of pixels havingpixel values of the quantization value (Q+1) and areas with a smallerratio of pixels having pixel values of the quantization value Q) areproduced, and the pixel values of the coarse and dense areas seem tosmoothly change because of the integration effect of the visual sense ofhuman.

As a result, although 4 bits can represent only 16 levels, in the 4-bitimage after gradation conversion by the gradation conversion unit 45,pseudo representation of 256 levels can be realized as if the image werethe 8-bit target image before gradation conversion.

Next, FIG. 11 shows a configuration example of the dither addition part51 in FIG. 7.

In FIG. 11, the dither addition part 51 includes a calculation part 61,an HPF (High Pass Filter) 62, a random noise output part 63, and acoefficient setting part 64.

To the calculation part 61, the pixel values IN(x,y) of the target imagefrom the noise reduction unit 44 (FIG. 6) are supplied in the sequenceof raster scan as has been described in FIG. 8. Further, to thecalculation part 61, output of the HPF 62 is supplied.

The calculation part 61 adds the output of the HPF to the pixel valuesIN(x,y) of the target image, and supplies the resulting additionalvalues as dithered pixel values F(x,y) to the one-dimensional ΔΣmodulation part 52 (FIG. 7).

The HPF 62 filters the random noise output by the random noise outputpart 63 based on a filter coefficient set by the coefficient settingpart 64, and supplies the high-frequency component of the random noiseobtained as a result of filtering to the calculation part 61.

The random noise output part 63 generates random noise according to aGaussian distribution or the like, for example, and outputs it to theHPF 62.

The coefficient setting part 64 determines the filter coefficient of theHPF 62 based on the spatial frequency characteristic of the visual senseof human and the resolution of the display unit 47 (FIG. 6) and sets itin the HPF 62.

That is, the coefficient setting part 64 stores the spatial frequencycharacteristic of the visual sense of human. Further, the coefficientsetting part 64 acquires the resolution of the display unit 47 from thedisplay control unit 46 (FIG. 6). Then, the coefficient setting part 64determines the filter coefficient of the HPF 62 in a manner as will bedescribed below from the spatial frequency characteristic of the visualsense of human and the resolution of the display unit 47, and sets it inthe HPF 62.

Note that the coefficient setting part 64 adjusts the filter coefficientof the HPF 62 in response to the operation of a user or the like.Thereby, the user can adjust the image quality of the image aftergradation conversion in the gradation conversion unit 45 to desiredimage quality.

In the dither addition part 51 having the above described configuration,the coefficient setting part 64 determines the filter coefficient of theHPF 62 from the spatial frequency characteristic of the visual sense ofhuman and the resolution of the display unit 47, and sets it in the HPF62.

Then, the HPF 62 performs product-sum operation of the filtercoefficient set by the coefficient setting part 64 and the random noiseoutput by the random noise output part 63 or the like, and thereby,filters the random noise output by the random noise output part 63 andsupplies the high-frequency component of the random noise to thecalculation part 61.

The calculation part 61 adds the 12-bit pixel values IN(x,y) of thetarget image from the noise reduction unit 44 (FIG. 6) and thehigh-frequency component of the random noise from the HPF 62, andsupplies resulting 12-bit additional values with the same bit number asthat of the target image (or additional values with the larger bitnumber) as dithered pixel values F(x,y) to the one-dimensional ΔΣmodulation part (FIG. 7).

Next, a method of determining the filter coefficient of the HPF 62 basedon the spatial frequency characteristic of the visual sense of human andresolution of the display unit performed in the coefficient setting part64 will be explained referring to FIGS. 12 to 14B.

FIG. 12 shows the spatial frequency characteristic of the visual senseof human.

In FIG. 12, the horizontal axis indicates the spatial frequency and thevertical axis indicates the sensitivity of the visual sense of human.

As shown in FIG. 12, the sensitivity of the visual sense of humansteeply rises as the spatial frequency increases from 0 cycle/degree tohigher, becomes the maximum around 9 cycles/degree, and then, becomeslower as the frequency becomes higher.

Here, FIG. 13 is a diagram for explanation of the unit cycle/degree ofthe spatial frequency.

cycle/degree expresses the number of stripes seen in a range of a unitangle of a viewing angle. For example, 10 cycles/degree expresses that10 pairs of white lines and black lines are seen in the range of theviewing angle of one degree and 20 cycles/degree expresses that 20 pairsof white lines and black lines are seen in the range of the viewingangle of one degree.

Since the image after gradation conversion by the gradation conversionunit 45 is finally displayed on the display unit 47 (FIG. 6), in view ofimprovement of the image quality of the image displayed on the displayunit 47, only (from 0 cycle/degree) to the highest spatial frequency ofthe image displayed on the display unit 47 may be enough to beconsidered with respect to the spatial frequency characteristic of thevisual sense of human.

Accordingly, the coefficient setting part 64 (FIG. 11) determines thefilter coefficient of the HPF 62 based on the characteristic equal to orless than the spatial frequency corresponding to the resolution of thedisplay unit among the spatial frequency characteristics of the visualsense of human.

That is, the highest spatial frequency of the image displayed on thedisplay unit 47 can be obtained in the spatial frequency in units ofcycle/degree from a distance from a viewer to the display unit 47(hereinafter, also referred to as “viewing distance”) when the imagedisplayed on the display unit 47 is viewed.

If the (longitudinal) length in the vertical direction of the displayunit 47 is expressed by H inches, about 2.5H to 3.0H of the viewingdistance is employed, for example.

For instance, when the display unit 47 has a display screen in a size of40 inches of lateral and longitudinal pixels of 1920×1080 for display ofa so-called full-HD (High Definition) image, the highest spatialfrequency of the image displayed on the display unit 47 is about 30cycles/degree.

Here, the highest spatial frequency of the image displayed on thedisplay unit 47 is determined by the resolution of the display unit 47,and also appropriately referred to as “spatial frequency correspondingto resolution”.

FIGS. 14A and 14B show a method of determining the filter coefficient ofthe HPF 62 based on the characteristic equal to or less than the spatialfrequency characteristic corresponding to the resolution of the displayunit 47 among the spatial frequency characteristics of the visual senseof human by the coefficient setting part 64 (FIG. 11).

That is, FIG. 14A shows the characteristic equal to or less than thespatial frequency to the resolution of the display unit 47 among thespatial frequency characteristics of the visual sense of human.

Here, FIG. 14A shows, assuming that the spatial frequency to theresolution of the display unit 47 is 30 cycles/degree, for example, thecharacteristic equal to or less than 30 cycles/degree among the spatialfrequency characteristics of the visual sense of human shown in FIG. 12.

The coefficient setting part 64 determines the filter coefficient of theHPF 62 based on the spatial frequency characteristic of the visual senseof human in FIG. 14A so that the characteristic at high frequencies ofthe amplitude characteristics of the HPF 62 may be a characteristicopposite to the spatial frequency characteristic of the visual sense ofhuman in FIG. 14A (the characteristic depicting the shape of verticalinversion of the spatial frequency characteristic of the visual sense).

That is, FIG. 14B shows the amplitude characteristic of the HPF 62having the filter coefficient determined in the above described manner.

The amplitude characteristic in FIG. 14B has the maximum gain (e.g., 0db) at 30 cycles/degree as the spatial frequency corresponding to theresolution of the display unit 47, and the characteristic at highfrequencies is the characteristic of the HPF as the characteristicopposite to the spatial frequency characteristic of the visual sense ofhuman in FIG. 14A (hereinafter, also appropriately referred to as“opposite characteristic”).

Therefore, in the HPF 62 (FIG. 11) having the amplitude characteristicin FIG. 14B, more of the higher frequency components at which thesensitivity of the visual sense of human is lower of the random noisefrom the random noise output part 63 pass and the frequency componentscorresponding to around 9 cycles/degree at which the sensitivity of thevisual sense of human is higher and less than 9 cycles/degree are cut.

As a result, in the calculation part 61 (FIG. 11), to the pixel valuesIN(x,y) of the target image, the frequency components at which thesensitivity of the visual sense of human is higher of the random noiseis (hardly) added but more of the higher frequency components at whichthe sensitivity of the visual sense of human is lower are added.Accordingly, in the image after gradation conversion by the gradationconversion unit 45, visual recognition of noise can be prevented andvisual image quality can be improved.

Note that, the amplitude characteristic of the HPF at the highfrequencies does not necessarily completely match the characteristicopposite to the visual sense of human. That is, the amplitudecharacteristic of the HPF 62 at the high frequencies may be enough to besimilar to the characteristic opposite to the visual sense of human.

Further, as the filter that filters the random noise output by therandom noise output part 63 (hereinafter, also referred to as “noisefilter”), in place of the HPF 62, a filter having a whole amplitudecharacteristic that is inverse of the spatial frequency characteristicof the visual sense of human in FIG. 14A may be employed.

That is, according to the spatial frequency characteristic of the visualsense of human in FIG. 14A, as frequency components at which thesensitivity of the visual sense of human is lower, there are not onlythe high-frequency components but also the frequency components at thelow frequencies around 0 cycle/degree. As the noise filter, a bandpassfilter that passes the high- and low-frequency components of the randomnoise output by the random noise output part 63 may be employed.

Note that, when the bandpass filter is employed as the noise filter, thenumber of taps of the noise filter becomes greater and the device isincreased in size and cost.

Further, according to a simulation performed by the inventors of theinvention, even when the above described bandpass filter is employed asthe noise filter, compared to the case of employing the HPF 62, nosignificant improvement is recognized in the image quality of the imageafter gradation conversion.

Furthermore, when the above described bandpass filter is employed as thenoise filter, not only the high-frequency components but also thelow-frequency components are added to the pixel values IN(x,y) of thetarget image. As a result, in some cases, in the coarse and dense areasdescribed in FIGS. 10A and 10B, parts in which many of pixels havingpixel values of the quantization value Q or pixels having pixel valuesof the quantization value (Q+1) continue are produced, and consequently,unnatural lines may appear in the image after gradation conversion.

Therefore, in view of the size and cost of the device and also in viewof the image quality of the image after gradation conversion, it isdesirable that the HPF 62 having the amplitude characteristic at highfrequencies of the characteristic opposite to the visual sense of humanas shown in FIG. 14B is employed.

Next, FIG. 15 shows a configuration example of the one-dimensional ΔΣmodulation part 52 in FIG. 7.

In the drawing, the same signs are assigned to the parts correspondingto those in the gradation conversion devices as the two-dimensional ΔΣmodulator in FIG. 5A.

In FIG. 15, the one-dimensional ΔΣ modulation part 52 includes acalculation part 31, a quantization part 32, a calculation part 33, aone-dimensional filter 71, and a coefficient setting part 72.

To the calculation part 31, the pixel values F(x,y) of the ditheredtarget image are supplied from the dither addition part 51 (FIG. 7) inthe sequence of raster scan. Further, to the calculation part 31, outputof the one-dimensional filter 71 is supplied.

The calculation part 31 adds the pixel values F(x,y) from the ditheraddition part 51 and the output of the one-dimensional filter 71, andsupplies the resulting additional values to the quantization part 32 andthe calculation part 33.

The quantization part 32 quantizes the additional values from thecalculation part 31 into 8 bits as the bit number of the image to bedisplayed on the display unit 47 (FIG. 6), and supplies the resulting8-bit quantization values (quantization values containing quantizationerrors −Q(x,y)) as the pixel values OUT(x,y) of the pixels (x,y) of theimage after gradation conversion to the calculation part 33 and thedisplay control unit 46 (FIG. 6).

Here, the one-dimensional ΔΣ modulation part 52 acquires the bit numberof the image to be displayed by the display unit 47 from the displaycontrol unit 46 and controls the quantization part 32 to performquantization into the quantization values in the bit number.

The calculation part 33 obtains quantization errors −Q(x,y) produced bythe quantization in the quantization part by subtracting the pixelvalues OUT(x,y) from the quantization part 32 from the additional valuesfrom the calculation part 31, that is, subtracting the output from thequantization part 32 from the input to the quantization part 32, andsupplies them to the one-dimensional filter 71.

The one-dimensional filter 71 is a one-dimensional filter that filterssignals, and filters the quantization errors −Q(x,y) from thecalculation part 33 and outputs the filtering results to the calculationpart 31.

Here, in the calculation part 31, the filtering results of thequantization errors −Q(x,y) output by the one-dimensional filter 71 andthe pixel values IN(x,y) are added in the above described manner.

The coefficient setting part 72 determines the filter coefficient of theone-dimensional filter 71 based on the spatial frequency characteristicof the visual sense of human and the resolution of the display unit 47(FIG. 6) and sets it in the one-dimensional filter 71.

That is, the coefficient setting part 72 stores the spatial frequencycharacteristic of the visual sense of human. Further, the coefficientsetting part 72 acquires the resolution of the display unit 47 from thedisplay control unit 46 (FIG. 6). Then, the coefficient setting part 72determines the filter coefficient of the one-dimensional filter 71 fromthe spatial frequency characteristic of the visual sense of human andthe resolution of the display unit in a manner described as below andsets it in the one-dimensional filter 71.

Note that the coefficient setting part 72 adjusts the filter coefficientof the one-dimensional filter 71 in response to user operation or thelike. Thereby, the user can adjust the image quality of the image aftergradation conversion in the gradation conversion unit 45 to desiredimage quality.

In the one-dimensional ΔΣ modulation part 52 having the above describedconfiguration, the coefficient setting part 72 determines the filtercoefficient of the one-dimensional filter 71 from the spatial frequencycharacteristic of the visual sense of human and the resolution of thedisplay unit 47 and sets it in the one-dimensional filter 71.

Then, the one-dimensional filter 71 performs product-sum operation ofthe filter coefficient set by the coefficient setting part 72 and thequantization errors −Q(x,y) output by the calculation part 33 or thelike, and thereby, filters the quantization errors −Q(x,y) output by thecalculation part 33 and supplies the high-frequency component of thequantization errors −Q(x,y) to the calculation part 31.

The calculation part 31 adds the pixel values F(x,y) from the ditheraddition part 51 and the output of the one-dimensional filter 71, andsupplies the resulting additional values to the quantization part 32 andthe calculation part 33.

The quantization part 32 quantizes the additional values from thecalculation part 31 into 8 bits as the bit number of the image to bedisplayed on the display unit 47 (FIG. 6), and supplies the resulting8-bit quantization values as the pixel values OUT(x,y) of the pixels(x,y) of the image after gradation conversion to the calculation part 33and the display control unit 46 (FIG. 6).

The calculation part 33 obtains quantization errors −Q(x,y) contained inthe pixel values OUT(x,y) the quantization part 32 by subtracting thepixel values OUT(x,y) from the quantization part 32 from the additionalvalues from the calculation part 31, and supplies them to theone-dimensional filter 71.

The one-dimensional filter 71 filters the quantization errors −Q(x,y)from the calculation part 33 and outputs the filtering results to thecalculation part 31. In the calculation part 31, the filtering resultsof the quantization errors −Q(x,y) output by the one-dimensional filter71 and the pixel values IN(x,y) are added in the above described manner.

In the one-dimensional ΔΣ modulation part 52, the quantization errors−Q(x,y) are fed back to the input side (calculation part 31) via theone-dimensional filter 71, and thereby, the one-dimensional ΔΣmodulation is performed. Therefore, in the one-dimensional ΔΣ modulationpart 52, the one-dimensional ΔΣ modulation is performed on the pixelvalues F(x,y) from the dither addition part 51, and the pixel valuesOUT(x,y) are output as results of the one-dimensional ΔΣ modulation.

In the one-dimensional ΔΣ modulation part 52 in FIG. 15, thequantization errors −Q(x,y) are quantization errors corresponding to thepixel values F(x,y). To obtain the pixel values OUT(x,y) obtained by ΔΣmodulation of the pixel values F(x,y), the quantization errors −Q(x,y)for the pixel values F(x,y) are not used but the quantization errors forthe pixel values before the pixel values F(x,y) (pixel values processedbefore) are used in the sequence of raster scan.

That is, in the calculation part 31, the filtering results of theone-dimensional filter 71 using the quantization errors respectivelycorresponding to pixel values F(x−1,y), F(x−2,y), F(x−3,y), F(x−4,y),F(x−5,y) of five pixels, for example, which have been processedimmediately before the pixel values F(x,y), are added to the pixelvalues F(x,y).

Next, FIG. 16 shows a configuration example of the one-dimensionalfilter 71 in FIG. 15.

In FIG. 16, the one-dimensional filter 71 includes delay parts 81 ₁ to81 ₅, multiplication parts 82 ₁ to 82 ₅, and an addition part 83, andforms an FIR (Finite Impulse Response) filter with five taps.

That is, to the delay part 81 _(i) (i=1, 2, 3, 4, 5), stored values ofthe delay part 81 _(i−1) at the upstream are input. The delay part 81_(i) temporarily stores the input there, delays the input by a time forone pixel, and outputs it to the delay part 81 _(i+1) at the downstreamand the multiplication parts 82 _(i).

To the delay part 81 ₁ at the most upstream, the quantization errors−Q(x,y) from the calculation part 33 (FIG. 15) are supplied, and thedelay part 81 ₁ stores the quantization errors −Q(x,y) and delays.

Further, the delay part 81 ₅ at the most downstream outputs the delayedinput to the multiplication parts 82 ₅ only.

The multiplication part 82 _(i) multiplies the output of the delay part81 _(i) by a filter coefficient a(i) and supplies a resultingmultiplication value to the addition part 83.

The addition part 83 adds the multiplication values from the respectivemultiplication parts 82 ₁ to 82 ₅, and supplies a resulting additionalvalue as a result of filtering of the quantization errors −Q(x,y) to thecalculation part 31 (FIG. 15).

As described above, it is necessary for the one-dimensional filter 71 tohave delay parts 81 _(i) that store quantization errors of some (five inFIG. 16) pixels on one horizontal line, however, it is not necessary toprovide the line memory necessary for the two-dimensional filter 34 inFIG. 5A.

Therefore, according to the one-dimensional ΔΣ modulation part 52including such a one-dimensional filter 71, compared to thetwo-dimensional ΔΣ modulation part in FIG. 5A, downsizing and costreduction of the device can be realized.

Next, referring to FIGS. 17A and 17B, a method of determining the filtercoefficient of the one-dimensional filter 71 based on the spatialfrequency characteristic of the visual sense of human and the resolutionof the display unit performed by the coefficient setting part 72 in FIG.15 will be explained.

Now, if the additional values output by the calculation part 31 areexpressed by U(x,y) in the one-dimensional ΔΣ modulation part 52 in FIG.15, the following equations (1) and (2) hold in the one-dimensional ΔΣmodulation part 52.

−Q(x,y)=U(x,y)−OUT(x,y)  (1)

U(x,y)=F(x,y)+K×(−Q(x,y))  (2)

By substituting equation (2) into equation (1) and eliminating U(x,y),equation (3) is obtained.

OUT(x,y)=F(x,y)+(1−K)×Q(x,y)  (3)

Here, in equation (3), K represents a transfer function of theone-dimensional filter 71.

In ΔΣ modulation, noise shaping of, as it were, pushing the quantizationerrors toward the high frequencies is performed. In equation (3), thequantization errors Q(x,y) are modulated by (1−K), and the modulation isnoise shaping.

Therefore, the amplification characteristic of the noise shaping in theΔΣ modulation of the one-dimensional ΔΣ modulation part 52 is determinedby the property of the one-dimensional filter 71, i.e., the filtercoefficient of the one-dimensional filter 71.

Here, as described in FIG. 12, the sensitivity of the visual sense ofhuman steeply becomes the maximum around 9 cycles/degree, and then,becomes lower as the frequency becomes higher.

On the other hand, since the image of the gradation conversion by thegradation conversion unit 45 is finally displayed on the display unit 47(FIG. 6), in view of the improvement of the image quality of the imagedisplayed on the display unit 47, only to the spatial frequencycorresponding to the resolution of the display unit 47, i.e., thehighest spatial frequency of the image displayed on the display unit 47may be enough to be considered with respect to the spatial frequencycharacteristic of the visual sense of human.

Accordingly, the coefficient setting part 72 (FIG. 15) determines thefilter coefficient of the one-dimensional filter 71 based on thecharacteristic equal to or less than the spatial frequency correspondingto the resolution of the display unit 47 among the spatial frequencycharacteristics of the visual sense of human.

FIGS. 17A and 17B are diagrams for explanation of the method ofdetermining the filter coefficient of the one-dimensional filter 71based on the characteristic equal to or less than the spatial frequencycorresponding to the resolution of the display unit 47 among the spatialfrequency characteristics of the visual sense of human by thecoefficient setting part 72 (FIG. 15).

That is, FIG. 17A shows the characteristic equal to or less than thespatial frequency corresponding to the resolution of the display unit 47among the spatial frequency characteristics of the visual sense ofhuman.

Here, FIG. 17A shows, assuming that the spatial frequency correspondingto the resolution of the display unit 47 is 30 cycles/degree, forexample, the characteristic equal to or less than 30 cycles/degree amongthe spatial frequency characteristics of the visual sense of human shownin FIG. 12. Therefore, FIG. 17A is the same diagram as FIG. 14Adescribed above.

The coefficient setting part 72 determines the filter coefficient of theone-dimensional filter 71 based on the spatial frequency characteristicof the visual sense of human in FIG. 17A so that the characteristic athigh frequencies of the amplitude characteristics of the noise shapingdetermined by the characteristic of the one-dimensional filter 71 may bethe characteristic opposite to the spatial frequency characteristic ofthe visual sense of human in FIG. 17A.

That is, FIG. 17B shows the amplitude characteristic of the noiseshaping determined by the characteristic of the one-dimensional filter71 having the filter coefficient determined in the above describedmanner.

The amplitude characteristic in FIG. 17B has the maximum gain at 30cycles/degree as the spatial frequency corresponding to the resolutionof the display unit 47, and the characteristic at high frequencies isthe characteristic of the HPF as the characteristic opposite to thevisual sense of human in FIG. 17A.

Therefore, according to the noise shaping having the amplitudecharacteristic in FIG. 17B, the higher frequency components at which thesensitivity of the visual sense of human is lower of the quantizationerrors contained in the pixel values OUT(x,y) of the image aftergradation conversion become larger and the frequency componentscorresponding to around 9 cycles/degree at which the sensitivity of thevisual sense of human is higher and less than 9 cycles/degree becomesmaller.

As a result, in the image after gradation conversion by the gradationconversion unit 45, visual recognition of noise can be prevented andvisual image quality can be improved.

Note that, the amplitude characteristic of the noise shaping at highfrequencies does not necessarily completely match the characteristicopposite to the visual sense of human as is the case of the HPF 62 (FIG.11) described in FIG. 14B. That is, the amplitude characteristic of thenoise shaping at high frequencies may be enough to be similar to thecharacteristic opposite to the visual sense of human.

Further, the whole amplitude characteristic of the noise shaping at highfrequencies may be the characteristic opposite to the spatial frequencycharacteristic of the visual sense of human in FIG. 17A as is the caseof the HPF 62 described in FIG. 14B. Note that, as is the case of theHPF 62 described in FIG. 14B, in view of the size and cost of the deviceand also in view of the image quality of the image after gradationconversion, it is desirable that the characteristic of the HPF havingthe amplitude characteristic at high frequencies of the characteristicopposite to the visual sense of human as shown in FIG. 17B is employedas the amplitude characteristic of the noise shaping.

Here, the one-dimensional filter 71 that determines the amplitudecharacteristic of the noise shaping has five delay parts 81 ₁ to 81 ₅,as shown in FIG. 16, for example, and therefore, in the one-dimensionalfilter 71, the values to be added to the pixel values F(x,y) of thepixels (x,y) supplied to the calculation part 31 are obtained using thequantization errors for the pixel values of the five pixels processedimmediately before the pixels (x,y) (hereinafter, also referred toimmediately preceding processed pixels).

If the immediately preceding processed pixels are pixels on thehorizontal line on which the pixels (x,y) are, generally, the pixel(x,y) may be correlated with the immediately preceding processed pixels.However, if the immediately preceding processed pixels are on ahorizontal line different from that on which the pixels (x,y) are, i.e.,if the pixels (x,y) are pixels at the head of the horizontal line, itmay be possible that there is no correlativity between the pixels (x,y)and all of the immediately preceding processed pixels.

Since it is apparently not preferable that the values to be added to thepixel values F(x,y) of the pixels (x,y) are obtained using thequantization errors for the pixel values of the immediately precedingprocessed pixels not correlated with the pixels (x,y) in theone-dimensional filter 71, it is considered that the stored values ofthe five delay parts 81 ₁ to 81 ₅ of the one-dimensional filter 71 areinitialized to a fixed value of zero or the like, for example, in thehorizontal flyback period (and vertical flyback section) of the(dithered) image supplied from the dither addition part 51 (FIG. 7) tothe calculation part 31.

However, according a simulation performed by the inventors of theinvention, it is confirmed that the image (image after gradationconversion) with better image quality can be obtained in the case wherethe stored values of the delay parts 81 ₁ to 81 ₅ of the one-dimensionalfilter 71 are not initialized but stored without change in the delayparts 81 ₁ to 81 ₅ in the horizontal flyback period than in the case ofinitialization to the fixed value.

Therefore, in the one-dimensional filter 71, it is desirable that, inthe horizontal flyback period of the dithered image, the stored valuesof the delay parts 81 _(i) are not initialized but stored in the delayparts 81 _(i) without change.

Note that it is considered that the image with better image quality canbe obtained in the case where the stored values of the delay parts 81_(i) are not initialized to the fixed value but stored without changebecause the diffusivity of the quantization errors becomes better thanin the case of initialization to the fixed value.

Therefore, in view of improvement in the diffusivity of the quantizationerrors, in the one-dimensional filter 71, not only that the storedvalues of the delay parts 81 _(i) are not initialized in the horizontalflyback period but also the stored values of the delay parts 81 _(i) maybe initialized by random numbers.

That is, FIG. 18 shows another configuration example of theone-dimensional filter 71 in FIG. 15.

In the drawing, the same signs are assigned to the parts correspondingto those in the case of FIG. 16, and the description thereof will beappropriately omitted as below.

In FIG. 18, the one-dimensional filter 71 has the same configuration asthat in the case of FIG. 16 except that a random number output part 84and a switch 85 are newly provided.

The random number output part 84 generates and outputs random numbersthat can be taken as quantization errors −Q(x,y) obtained by thecalculation part 33 (FIG. 15).

The switch 85 selects the output of the random number output part 84 inthe horizontal flyback period (and vertical flyback period), and selectsthe quantization errors −Q(x,y) from the calculation part 33 (FIG. 15)and supplies them to the delay part 81 ₁ in other periods.

In the one-dimensional filter 71 in FIG. 18, in periods other than thehorizontal flyback period, the switch selects the quantization errors−Q(x,y) from the calculation part 33 and supplies them to the delay part81 ₁, and thereby, the same filtering as that in the case of FIG. 16 isperformed.

On the other hand, in the period of the horizontal flyback period, theswitch 85 selects the output of the random number output part 84 and therandom number output part 84 sequentially supplies five random numbersto the delay part 81 ₁. Thereby, (5−i+1)th random number is stored inthe delay part 81 _(i), and, regarding the pixels at the head of thehorizontal line after the horizontal flyback period ends, in thehorizontal flyback period, the output of the one-dimensional filter 71as the values to be added in the calculation part 31 (FIG. 15) areobtained using the random numbers stored in the delay parts 81 ₁ to 81₅.

Note that, in the horizontal flyback period, the output from theone-dimensional filter 71 to the calculation part 31 is not performed.

As described above, in the gradation conversion unit (FIG. 7), randomnoise is added to the pixel values forming the image and the image isdithered in the dither addition part 51, one-dimensional ΔΣ modulationis performed on the dithered image in the one-dimensional ΔΣ modulationpart 52, and thereby, gradation conversion can be performed withoutusing a line memory and high quality image can be obtained as the imageafter gradation conversion.

Therefore, the gradation conversion that provides the high quality imagecan be performed without using a line memory, and downsizing and costreduction of the device can be realized.

That is, since the gradation conversion is performed without using aline memory, not the two-dimensional ΔΣ modulation, but theone-dimensional ΔΣ modulation is performed in the gradation conversionunit 45.

Since the one-dimensional ΔΣ modulation is performed on the pixel valuessupplied in the sequence of raster scan in the one-dimensional ΔΣmodulation part 52 in the image after one-dimensional ΔΣ modulation, theeffect of ΔΣ modulation (effect of noise shaping) is produced in thehorizontal direction but the effect of ΔΣ modulation is not produced inthe vertical direction.

Accordingly, only by the one-dimensional ΔΣ modulation, apparent graylevels are poor with respect to the vertical direction of the imageafter one-dimensional ΔΣ modulation, and quantization noise(quantization errors) is highly visible.

On this account, dither is performed before one-dimensional ΔΣmodulation in the gradation conversion unit 45. As a result, in theimage after gradation conversion by the gradation conversion unit 45,the effect of dithering is produced in the vertical direction, theeffect of one-dimensional ΔΣ modulation is produced in the horizontaldirection, and thereby, apparent image quality can be improved withrespect to both the horizontal directions and vertical direction.

Further, in the gradation conversion unit 45, the high-frequencycomponents of the random noise obtained by filtering the random noisewith the HPF 62 are used for dithering. Furthermore, the filtercoefficient of the HPF 62 is determined based on the characteristicequal to or less than the spatial frequency corresponding to theresolution of the display unit 47 (FIG. 6) among the spatial frequencycharacteristics of the visual sense of human so that the characteristicat high frequencies of the amplitude characteristics of the HPF 62 maybe the characteristic opposite to the spatial frequency characteristicof the visual sense of human.

Therefore, the frequency components of noise used for dithering arefrequency components at which the sensitivity of the visual sense ofhuman is lower, and the apparent image quality of the image aftergradation conversion can be improved.

Further, in the gradation conversion unit 45, the filter coefficient ofthe one-dimensional filter 71 (FIG. 15) is determined based on thecharacteristic equal to or less than the spatial frequency correspondingto the resolution of the display unit 47 among the spatial frequencycharacteristics of the visual sense of human so that the characteristicat high frequencies of the amplitude characteristics of the noiseshaping of the quantization errors may be the characteristic opposite tothe spatial frequency characteristic of the visual sense of human.

Therefore, the frequency components of quantization errors are frequencycomponents at which the sensitivity of the visual sense of human islower, and the apparent image quality of the image after gradationconversion can be improved.

Note that the dither addition part 51 (FIG. 11) can be formed withoutthe HPF 62 (and the coefficient setting part 64) provided, and, in thiscase, the size of the device can be made smaller. In this case, theapparent image quality of the image after gradation conversion becomeslower compared to the case where the HPF 62 is provided.

Further, if the image as a target image of gradation conversion (targetimage) in the gradation conversion unit 45 has plural components of Y,Cb, Cr, etc. as pixel values, the gradation conversion processing isperformed independently with respect to each component. That is, if thetarget image has a Y-component, a Cb-component, and a Cr-component asthe pixel values, the gradation conversion processing is performed onlyon the Y-component. In the same manner, the gradation conversionprocessing is performed only on the Cb-component, and the gradationconversion processing is performed only on the Cr-component.

As above, the case where the invention is applied to gradationconversion in a TV has been described, however, the embodiment of theinvention can be applied to any device that handles images other thanthose of the TV.

That is, for example, in HDMI(R) (High-Definition Multimedia Interface)that has rapidly spread recently, Deep Color that transmits not only8-bit pixel values but also 10-bit or 12-bit pixel values are specified,and the gradation conversion processing by the gradation conversion unit45 can apply the images having 10-bit or 12-bit pixel values transmittedvia the HDMI to gradation conversion when the images are displayed on adisplay that displays 8-bit images or the like.

Further, for example, in the case where a video device that reproduces adisc such as a Blu-ray (R) disc or the like reproduces a 12-bit image,for example, when images are displayed on a display that displays 8-bitimages from the video device via a transmission path for transmitting8-bit images, gradation conversion processing by the gradationconversion unit 45 is performed in the video device, 12-bit images areconverted into 8-bit images and transmitted to the display, and thereby,pseudo display of the 12-bit images can be performed on the display.

Next, the amplitude characteristic of the HPF 62 (FIG. 11) and theamplitude characteristic of noise shaping using the one-dimensionalfilter 71 (FIG. 15) will be further explained later, but first, theerror diffusion method in related art, i.e., the two-dimensional ΔΣmodulation in related art will be described.

FIG. 19 shows amplitude characteristics of noise shaping by thetwo-dimensional ΔΣ modulation in related art.

As the two-dimensional filter 34 in FIG. 5A used for noise shaping bythe two-dimensional ΔΣ modulation in related art, there are a Jarvis,Judice & Ninke filter (hereinafter, also referred to as “Jarvis filter”)and a Floyd & Steinberg filter (hereinafter, also referred to as “Floydfilter”).

FIG. 19 shows the amplitude characteristic of noise shaping using theJarvis filter and the amplitude characteristic of noise shaping usingthe Floyd filter.

Here, in FIG. 19, the spatial frequency corresponding to the resolutionof the display unit 47 (FIG. 6) (the highest spatial frequency of theimage that can be displayed on the display unit 47) is set to about 30cycles/degree like in the cases of FIGS. 14B and 17B.

Further, FIG. 19 also shows the spatial frequency characteristic of thevisual sense of human (hereinafter, also referred to as “visualcharacteristic”) in addition to the amplitude characteristics of noiseshaping.

The vertical axes (gain) of the amplitude characteristic of the HPF 62in FIG. 14B and the amplitude characteristic of noise shaping using theone-dimensional ΔΣ modulation in FIG. 17B are expressed by db (decibel),however, the vertical axis of the amplitude characteristic in FIG. 19 islinearly expressed. The expression is the same in FIG. 20 described asbelow.

Further, the Jarvis filter is a two-dimensional filter, and there arespatial frequencies in two directions of the horizontal direction andthe vertical direction as (the axes of) the spatial frequency of theamplitude characteristic of noise shaping using the Jarvis filter. InFIG. 19 (the same in FIG. 20), the spatial frequency in one direction ofthe two directions is the horizontal axis. The expression is the samefor the spatial frequency of the amplitude characteristic of noiseshaping using the Floyd filter.

If the spatial frequency corresponding to the resolution of the displayunit 47 takes an extremely high value of about 120 cycles/degree, forexample, noise (quantization errors) is sufficiently modulated in thefrequency band in which the sensitivity of the visual sense of human islower with the Jarvis filter or the Floyd filter.

Note that, if the spatial frequency corresponding to the resolution ofthe display unit 47 takes about 30 cycles/degree, for example, it isdifficult to sufficiently modulate noise in the high frequency band inwhich the sensitivity of the visual sense of human is lower with theJarvis filter or the Floyd filter.

In this case, noise is highly visible and apparent image quality isdeteriorated in the image after gradation conversion.

In order to reduce the deterioration of the apparent image qualitybecause the noise is highly visible in the image after gradationconversion, it is necessary to set the amplitude characteristic of noiseshaping as shown in FIG. 20, for example.

That is, FIG. 20 shows an example of the amplitude characteristic ofnoise shaping for reducing the deterioration of the apparent imagequality because the noise is highly visible in the image after gradationconversion (hereinafter, also referred to as “deterioration reducingnoise shaping”).

Here, a filter for noise shaping used for ΔΣ modulation that realizesdeterioration reducing noise shaping (a filter corresponding to thetwo-dimensional filter 34 in FIG. 5A and the one-dimensional filter 71in FIG. 15) is also called an SBM (Super Bit Mapping) filter.

FIG. 20 shows the visual characteristic, the amplitude characteristic ofnoise shaping using the Jarvis filter, and the amplitude characteristicof noise shaping using the Floyd filter shown in FIG. 19 in addition tothe amplitude characteristic of deterioration reducing noise shaping(noise shaping using the SBM filter).

In the amplitude characteristic of deterioration reducing noise shaping,the characteristic at high frequencies is the characteristic opposite tothe visual characteristic like the amplitude characteristic of the HPF62 in FIG. 14B and the amplitude characteristic of noise shaping in FIG.17B.

Furthermore, the amplitude characteristic of deterioration reducingnoise shaping increases at high frequencies more rapidly than theamplitude characteristic of noise shaping using the Jarvis filter or theFloyd filter.

Thereby, in the deterioration reducing noise shaping, noise(quantization errors) is modulated toward the higher frequencies atwhich the sensitivity of the visual sense of human is lower than in thenoise shaping using the Jarvis filter or the Floyd filter.

By determining the filter coefficient of the one-dimensional filter 71so that the amplitude characteristic of noise shaping using theone-dimensional filter 71 in FIG. 15 may be the characteristic oppositeto the visual characteristic at high frequencies and may increase morerapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter like theamplitude characteristic of noise shaping (deterioration reducing noiseshaping) using the SBM filter, in the calculation part 31 in FIG. 15,noise (quantization errors) at high frequencies at which visualsensitivity is lower is added to the pixel values F(x,y), and, as aresult, the noise (quantization errors) can be prevented from beinghighly visible in the image after gradation conversion.

Similarly, by determining the filter coefficient of the HPF 62 so thatthe amplitude characteristic of noise shaping using the HPF 62 in FIG.11 may be the characteristic opposite to the visual characteristic athigh frequencies and may increase more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter like the amplitude characteristic of noise shapingusing the SBM filter, in the calculation part 61 in FIG. 11, noise athigh frequencies at which visual sensitivity is lower is added, and, asa result, the noise (quantization errors) can be prevented from beinghighly visible in the image after gradation conversion.

FIGS. 21A to 23B show examples of amplitude characteristics of noiseshaping by ΔΣ modulation in the one-dimensional ΔΣ modulation part 52 inFIG. 15 and filter coefficients of the one-dimensional filter 71 whenthe highest spatial frequency of the image that can be displayed on thedisplay unit 47 (FIG. 6) is set to 30 cycles/degree.

Here, in FIGS. 21A to 23B (the same in FIGS. 24A to 26B, which will bedescribed later), the vertical axis of the amplitude characteristic isexpressed by dB.

Further, in FIGS. 21A to 23B, an FIR filter with two taps is employed asthe one-dimensional filter 71, and g(1) and g(2) express two filtercoefficients of the FIR filter with two taps.

The filter coefficient g(1) corresponds to the filter coefficient a(1)of the one-dimensional filter 71 with five taps shown in FIG. 16, andmultiplied by the quantization error of the pixel on the immediate leftof the pixel of interest. Further, the filter coefficient g(2)corresponds to the filter coefficient a(2) of the one-dimensional filter71 with five taps shown in FIG. 16, and multiplied by the quantizationerror of the pixel on the left of the immediate left of the pixel ofinterest.

FIGS. 21A and 21B show a first example of an amplitude characteristic ofnoise shaping by ΔΣ modulation in the one-dimensional ΔΣ modulation part52 in FIG. 15 and filter coefficients of the one-dimensional filter 71when the highest spatial frequency of the image that can be displayed onthe display unit 47 (FIG. 6) is set to 30 cycles/degree.

That is, FIG. 21A shows the first example of filter coefficients of theone-dimensional filter 71 (FIG. 15) with two taps determined so that theamplitude characteristic of noise shaping by ΔΣ modulation in theone-dimensional ΔΣ modulation part 52 may increase at high frequenciesmore rapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.

In FIG. 21A, as the filter coefficients of the one-dimensional filter 71with two taps, g(1)=0.9844 and g(2)=0.0391 are employed.

FIG. 21B shows the amplitude characteristic of noise shaping by ΔΣmodulation in the one-dimensional ΔΣ modulation part 52 when the filtercoefficients of the one-dimensional filter 71 are as shown in FIG. 21A.

In the amplitude characteristic of noise shaping of FIG. 21B, the gainincreases at high frequencies more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter.

FIGS. 22A and 22B show a second example of an amplitude characteristicof noise shaping by ΔΣ modulation in the one-dimensional ΔΣ modulationpart 52 in FIG. 15 and filter coefficients of the one-dimensional filter71 when the highest spatial frequency of the image that can be displayedon the display unit 47 (FIG. 6) is set to 30 cycles/degree.

That is, FIG. 22A shows the second example of filter coefficients of theone-dimensional filter 71 (FIG. 15) with two taps determined so that theamplitude characteristic of noise shaping by ΔΣ modulation in theone-dimensional ΔΣ modulation part 52 may increase at high frequenciesmore rapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.

In FIG. 22A, as the filter coefficients of the one-dimensional filter 71with two taps, g(1)=0.9680 and g(2)=0.0320 are employed.

FIG. 22B shows the amplitude characteristic of noise shaping by ΔΣmodulation in the one-dimensional ΔΣ modulation part 52 when the filtercoefficients of the one-dimensional filter 71 are as shown in FIG. 22A.

In the amplitude characteristic of noise shaping of FIG. 22B, the gainincreases at high frequencies more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter.

FIGS. 23A and 23B show a third example of an amplitude characteristic ofnoise shaping by ΔΣ modulation in the one-dimensional ΔΣ modulation part52 in FIG. 15 and filter coefficients of the one-dimensional filter 71when the highest spatial frequency of the image that can be displayed onthe display unit 47 (FIG. 6) is set to 30 cycles/degree.

That is, FIG. 23A shows the third example of filter coefficients of theone-dimensional filter 71 (FIG. 15) with two taps determined so that theamplitude characteristic of noise shaping by ΔΣ modulation in theone-dimensional ΔΣ modulation part 52 may increase at high frequenciesmore rapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.

In FIG. 23A, as the filter coefficients of the one-dimensional filter 71with two taps, g(1)=0.9751 and g(2)=0.0249 are employed.

FIG. 23B shows the amplitude characteristic of noise shaping by ΔΣmodulation in the one-dimensional ΔΣ modulation part 52 when the filtercoefficients of the one-dimensional filter 71 are as shown in FIG. 23A.

In the amplitude characteristic of noise shaping of FIG. 23B, the gainincreases at high frequencies more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter.

FIGS. 24A to FIG. 26B show examples of amplitude characteristics andfilter coefficients of the HPF 62 in FIG. 11 when the highest spatialfrequency of the image that can be displayed on the display unit 47(FIG. 6) is set to 30 cycles/degree.

Here, in FIGS. 24A to 26B, an FIR filter with three taps is employed asthe HPF 62, and h(1), h(2), and h(3) express three filter coefficientsof the FIR filter with three taps.

The filter coefficients h(1), h(2), and h(3) are multiplied by threecontinuous values of noise in the FIR filter with three taps as the HPF.

FIGS. 24A and 24B show a first example of an amplitude characteristic ofthe HPF 62 in FIG. 11 and filter coefficients of the HPF 62 when thehighest spatial frequency of the image that can be displayed on thedisplay unit 47 (FIG. 6) is set to 30 cycles/degree.

That is, FIG. 24A shows the first example of filter coefficients of theHPF 62 (FIG. 11) with three taps determined so that the amplitudecharacteristic of the HPF 62 may increase at high frequencies morerapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.

In FIG. 24A, as the filter coefficients of the HPF 62 with three taps,h(1)=h(3)=−0.0703, h(2)=0.8594 are employed.

FIG. 24B shows the amplitude characteristic of the HPF 62 when thefilter coefficients of the HPF 62 are as shown in FIG. 24A.

In the amplitude characteristic of noise shaping of FIG. 24B, the gainincreases at high frequencies more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter.

FIGS. 25A and 25B show a second example of an amplitude characteristicof the HPF 62 in FIG. 11 and filter coefficients of the HPF 62 when thehighest spatial frequency of the image that can be displayed on thedisplay unit 47 (FIG. 6) is set to 30 cycles/degree.

That is, FIG. 25A shows the second example of filter coefficients of theHPF 62 (FIG. 11) with three taps determined so that the amplitudecharacteristic of the HPF 62 may increase at high frequencies morerapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.

In FIG. 25A, as the filter coefficients of the HPF 62 with three taps,h(1)=h(3)=−0.0651, h(2)=0.8698 are employed.

FIG. 25B shows the amplitude characteristic of the HPF 62 when thefilter coefficients of the HPF 62 are as shown in FIG. 25A.

In the amplitude characteristic of noise shaping of FIG. 25B, the gainincreases at high frequencies more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter.

FIGS. 26A and 26B show a third example of an amplitude characteristic ofthe HPF 62 in FIG. 11 and filter coefficients of the HPF 62 when thehighest spatial frequency of the image that can be displayed on thedisplay unit 47 (FIG. 6) is set to 30 cycles/degree.

That is, FIG. 26A shows the third example of filter coefficients of theHPF 62 (FIG. 11) with three taps determined so that the amplitudecharacteristic of the HPF 62 may increase at high frequencies morerapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.

In FIG. 26A, as the filter coefficients of the HPF 62 with three taps,h(1)=h(3)=−0.0604, h(2)=0.8792 are employed.

FIG. 26B shows the amplitude characteristic of the HPF 62 when thefilter coefficients of the HPF 62 are as shown in FIG. 26A.

In the amplitude characteristic of noise shaping of FIG. 26B, the gainincreases at high frequencies more rapidly than the amplitudecharacteristic of noise shaping by ΔΣ modulation using the Floyd filteror the Jarvis filter.

Next, the above described series of processing may be performed byhardware or software. When the series of processing is performed bysoftware, a program forming the software is installed in ageneral-purpose computer or the like.

Accordingly, FIG. 27 shows a configuration example of one embodiment ofthe computer in which the program for executing the above describedseries of processing is installed.

The program may be recorded in a hard disk 105 and a ROM 103 asrecording media within the computer in advance.

Alternatively, the program may temporarily or permanently stored(recorded) in a removal recording medium 111 such as flexible disc,CD-ROM (Compact Disc Read Only Memory), MO (Magneto Optical) disc, DVD(Digital Versatile Disc), magnetic disc, and semiconductor memory. Sucha removal recording medium 111 may be provided as a so-called packagedsoftware.

Note that the program may be not only installed from the above describedremoval recording medium 111 in the computer but also installed in thehard disk 105 within the computer by wireless transfer from a downloadsite via an artificial satellite for digital satellite broadcasting orwired transfer via a network such as LAN (Local Area Network) or theInternet to the computer, and receiving the program transferred in thatway by a communication unit 108 in the computer.

The computer contains a CPU (Central Processing Unit) 102. Aninput/output interface 110 is connected via a bus 101 to the CPU 102,and, when a user inputs a command by operating an input unit 107including a keyboard, a mouse, a microphone, etc. or the like, the CPU102 executes the program stored in the ROM (Read Only Memory) 103according to the command via the input/output interface 110.Alternatively, the CPU 102 loads in a RAM (Random Access Memory) 104 theprogram stored in the hard disk 105, the program transferred from thesatellite or the network, received by the communication unit 108, andinstalled in the hard disk 105, and the program read out from theremovable recording medium 111 mounted on a drive 109 and installed inthe hard disk 105 and executes it. Thereby, the CPU 102 performsprocessing according to the above described flowchart or processingexecuted by the above described configuration in the block diagram.Then, the CPU 102 allows the processing result according to need to beoutput from an output unit 106 formed by an LCD (Liquid CrystalDisplay), speakers etc., or transmitted from the communication unit 108,and further recorded in the hard disk 105 via the input/output interface110, for example.

Here, in this specification, the processing steps for describing theprogram for allowing the computer to execute various processing may notnecessarily be processed in time sequence, but includes processing to beexecuted in parallel or individually (e.g., parallel processing orobject-based processing).

Further, the program may be processed by one computer ordistributed-processed by plural computers. Furthermore, the program maybe transferred to a remote computer and executed.

The embodiments of the invention are not limited to the above describedembodiments but various changes can be made without departing from thescope of the invention.

1. A gradation conversion device that converts a gradation of an image,comprising: dither means for dithering the image by adding random noiseto pixel values forming the image; and one-dimensional ΔΣ modulationmeans for performing one-dimensional ΔΣ modulation on the ditheredimage.
 2. The gradation conversion device according to claim 1, whereinthe dither means has an HPF (High Pass Filter) that filters signals,filters the random noise with the HPF, and adds a high-frequencycomponent of the random noise resulting from the filtering to the pixelvalues.
 3. The gradation conversion device according to claim 2, whereina filter coefficient of the HPF is determined so that a characteristicat high frequencies of an amplitude characteristic of the HPF is acharacteristic opposite to a spatial frequency characteristic of thevisual sense of human.
 4. The gradation conversion device according toclaim 3, wherein the filter coefficient of the HPF is determined so thatthe characteristic at high frequencies of the amplitude characteristicof the HPF may be the characteristic opposite to the spatial frequencycharacteristic of the visual sense of human based on a characteristicequal to or less than a spatial frequency corresponding to resolution ofdisplay means for displaying image on which the ΔΣ modulation has beenperformed among the spatial frequency characteristics of the visualsense of human.
 5. The gradation conversion device according to claim 4,wherein the filter coefficient of the HPF is determined so thatamplitude characteristic of the HPF may increase more rapidly than anamplitude characteristic of noise shaping by ΔΣ modulation using a Floydfilter or a Jarvis filter.
 6. The gradation conversion device accordingto claim 4, further comprising setting means for setting the filtercoefficient of the HPF based on the spatial frequency characteristic ofthe visual sense of human and the resolution of the display means. 7.The gradation conversion device according to claim 6, wherein thesetting means further adjusts the filter coefficient of the HPF inresponse to an operation by a user.
 8. The gradation conversion deviceaccording to claim 1, wherein the one-dimensional ΔΣ modulation meanshas: a one-dimensional filter that filters quantization errors;calculation means for adding the pixel values of the dithered image andoutput of the one-dimensional filter; and quantization means forquantizing output of the calculation means and outputs quantizationvalues containing the quantization errors as a result of one-dimensionalΔΣ modulation, wherein a filter coefficient of the one-dimensionalfilter is determined so that a characteristic at high frequencies of anamplitude characteristic of noise shaping performed by theone-dimensional ΔΣ modulation means may be a characteristic opposite tothe spatial frequency characteristic of the visual sense of human. 9.The gradation conversion device according to claim 8, wherein the filtercoefficient of the one-dimensional filter is determined so that thecharacteristic at high frequencies of the amplitude characteristic ofnoise shaping may be the characteristic opposite to the spatialfrequency characteristic of the visual sense of human based on acharacteristic equal to or less than the spatial frequency correspondingto the resolution of the display means for displaying the image on whichthe ΔΣ modulation has been performed among the spatial frequencycharacteristics of the visual sense of human.
 10. The gradationconversion device according to claim 9, wherein the filter coefficientof the one-dimensional filter is determined so that the characteristicat high frequencies of the amplitude characteristic of noise shapingperformed by the one-dimensional ΔΣ modulation means may increase morerapidly than the amplitude characteristic of noise shaping by ΔΣmodulation using the Floyd filter or the Jarvis filter.
 11. Thegradation conversion device according to claim 8, further comprisingsetting means for setting the filter coefficient of the one-dimensionalfilter based on the spatial frequency characteristic of the visual senseof human and the resolution of the display means.
 12. The gradationconversion device according to claim 11, wherein the setting meansfurther adjusts the filter coefficient of the one-dimensional filter inresponse to an operation by the user.
 13. The gradation conversiondevice according to claim 8, wherein the one-dimensional filter has:plural delay means for storing input and delaying; multiplication meansfor multiplying output of the plural delay means by the filtercoefficient, wherein the stored values of the delay means are notinitialized but stored without change in the delay means in a horizontalflyback period of the dithered image.
 14. The gradation conversiondevice according to claim 8, wherein the one-dimensional filter has:plural delay means for storing and delaying input; multiplication meansfor multiplying output of the plural delay means by the filtercoefficient, wherein the stored values of the delay means areinitialized by random numbers in a horizontal flyback period of thedithered image.
 15. A gradation conversion method of a gradationconversion device that converts a gradation of an image, comprising thesteps of: allowing the gradation conversion device to dither the imageby adding random noise to pixel values forming the image; and allowingthe gradation conversion device to perform one-dimensional ΔΣ modulationon the dithered image.
 16. A program allowing a computer to function asa gradation conversion device that converts a gradation of an image, theprogram allowing the computer to function as: dither means for ditheringthe image by adding random noise to pixel values forming the image; andone-dimensional ΔΣ modulation means for performing one-dimensional ΔΣmodulation on the dithered image.
 17. A gradation conversion device thatconverts a gradation of an image, comprising: a dither unit configuredto dither the image by adding random noise to pixel values forming theimage; and a one-dimensional ΔΣ modulation unit configured to performone-dimensional ΔΣ modulation on the dithered image.